eeg_backbone.Ours
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
💡 Tip: For seamless cloud logging and experiment tracking, try installing litlogger to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.
exp\intra-subject_Ours_ViT-bigG-14\sub-01/seed97
You are using a CUDA device ('NVIDIA GeForce RTX 4090 D') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high') which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\callbacks\model_checkpoint.py:881: Checkpoint directory exp\intra-subject_Ours_ViT-bigG-14\sub-01/seed97\checkpoints exists and is not empty.
Restoring states from the checkpoint path at C:\Users\zjy\Documents\code\exp\intra-subject_Ours_ViT-bigG-14\sub-01\seed97\checkpoints\last.ckpt
Traceback (most recent call last):
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 416, in
run_experiment(parse_args())
~~~~~~~~~~~~~~^^^^^^^^^^^^^^
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 411, in run_experiment
result = main(config, yaml)
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 346, in main
trainer.fit(pl_model, train_dataloaders=train_loader, val_dataloaders=val_loader, ckpt_path=ckpt_path)
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 584, in fit
call._call_and_handle_interrupt(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self,
^^^^^
...<6 lines>...
weights_only,
^^^^^^^^^^^^^
)
^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\call.py", line 49, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 630, in _fit_impl
self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1046, in _run
self._checkpoint_connector._restore_modules_and_callbacks(ckpt_path, weights_only)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\connectors\checkpoint_connector.py", line 411, in _restore_modules_and_callbacks
self.restore_model()
~~~~~~~~~~~~~~~~~~^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\connectors\checkpoint_connector.py", line 286, in restore_model
self.trainer.strategy.load_model_state_dict(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self._loaded_checkpoint,
^^^^^^^^^^^^^^^^^^^^^^^^
strict=self.trainer.lightning_module.strict_loading,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\strategies\strategy.py", line 372, in load_model_state_dict
self.lightning_module.load_state_dict(checkpoint["state_dict"], strict=strict)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\torch\nn\modules\module.py", line 2638, in load_state_dict
raise RuntimeError(
...<3 lines>...
)
RuntimeError: Error(s) in loading state_dict for PLModel:
size mismatch for brain.model.0.weight: copying a param with shape torch.Size([1280, 3417]) from checkpoint, the shape in current model is torch.Size([1280, 5950]).
eeg_backbone.Ours
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
💡 Tip: For seamless cloud logging and experiment tracking, try installing litlogger to enable LitLogger, which logs metrics and artifacts automatically to the Lightning Experiments platform.
exp\intra-subject_Ours_ViT-bigG-14\sub-01/seed97
You are using a CUDA device ('NVIDIA GeForce RTX 4090 D') that has Tensor Cores. To properly utilize them, you should set
torch.set_float32_matmul_precision('medium' | 'high')which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precisionC:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\callbacks\model_checkpoint.py:881: Checkpoint directory exp\intra-subject_Ours_ViT-bigG-14\sub-01/seed97\checkpoints exists and is not empty.
Restoring states from the checkpoint path at C:\Users\zjy\Documents\code\exp\intra-subject_Ours_ViT-bigG-14\sub-01\seed97\checkpoints\last.ckpt
Traceback (most recent call last):
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 416, in
run_experiment(parse_args())
~~~~~~~~~~~~~~^^^^^^^^^^^^^^
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 411, in run_experiment
result = main(config, yaml)
File "c:\Users\zjy\Documents\code\src\retrieval\train_process.py", line 346, in main
trainer.fit(pl_model, train_dataloaders=train_loader, val_dataloaders=val_loader, ckpt_path=ckpt_path)
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 584, in fit
call._call_and_handle_interrupt(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self,
^^^^^
...<6 lines>...
weights_only,
^^^^^^^^^^^^^
)
^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\call.py", line 49, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 630, in _fit_impl
self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1046, in _run
self._checkpoint_connector._restore_modules_and_callbacks(ckpt_path, weights_only)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\connectors\checkpoint_connector.py", line 411, in _restore_modules_and_callbacks
self.restore_model()
~~~~~~~~~~~~~~~~~~^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\trainer\connectors\checkpoint_connector.py", line 286, in restore_model
self.trainer.strategy.load_model_state_dict(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self._loaded_checkpoint,
^^^^^^^^^^^^^^^^^^^^^^^^
strict=self.trainer.lightning_module.strict_loading,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\pytorch_lightning\strategies\strategy.py", line 372, in load_model_state_dict
self.lightning_module.load_state_dict(checkpoint["state_dict"], strict=strict)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zjy\Documents\code.venv\Lib\site-packages\torch\nn\modules\module.py", line 2638, in load_state_dict
raise RuntimeError(
...<3 lines>...
)
RuntimeError: Error(s) in loading state_dict for PLModel:
size mismatch for brain.model.0.weight: copying a param with shape torch.Size([1280, 3417]) from checkpoint, the shape in current model is torch.Size([1280, 5950]).